Modeling of solar radiation using remote sensing and artificial neural network in Turkey

Artificial neural networks (ANNs) were used to estimate solar radiation in Turkey (26-45°E, 36-42°N) using geographical and satellite-estimated data. In order to train the Generalized regression neural network (GRNN) geographical and satellite-estimated data for the period from January 2002 to December 2002 from 19 stations spread over Turkey were used in training (ten stations) and testing (nine stations) data. Latitude, longitude, altitude, surface emissivity for e{open}4, surface emissivity for e{open}5, and land surface temperature are used in the input layer of the network. Solar radiation is the output. Root Mean Square Error (RMSE) and correlation coefficient (R2) between the estimated and measured values for monthly mean daily sum with ANN values have been found as 0.1630 MJ/m2 and 95.34% (training stations), 0.3200 MJ/m2 and 93.41% (testing stations), respectively. Since these results are good enough it was concluded that the developed GRNN tool can be used to predict the solar radiation in Turkey. © 2010 Elsevier Ltd

Solar radiation and precipitable water modeling for Turkey using artificial neural networks

Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water and solar radiation in a given location and given date (month), given altitude, temperature, pressure and humidity in Turkey (26–45ºE and 36–42ºN) during the period of 2000–2002. Resilient Propagation (RP) learning algorithms and logistic sigmoid transfer function were used in the network. To train the network, meteorological measurements taken by the Turkish State Meteorological Service (TSMS) and Wyoming University for the period from 2000 to 2002 from five stations distributed in Turkey were used as training data. Data from years (2000 and 2001) were used for training, while the year 2002 was used for testing and validating the model. The RP algorithm were first used for determination of the precipitable water and subsequently, computation of the solar radiation, in these stations Root Mean Square Error (RMSE) between the estimated and measured values for monthly mean daily sum for precipitable water and solar radiation values have been found as 0.0062 gr/cm2 and 0.0603 MJ/m2 (training cities), 0.5652 gr/cm2 and 3.2810 MJ/m2 (testing cities), respectively. © 2015, Springer-Verlag Wien

Estimation of solar radiation over Turkey using artificial neural network and satellite data

This study introduces artificial neural networks (ANNs) for the estimation of solar radiation in Turkey (26-45 E and 36-42 N). Resilient propagation (RP), Scale conjugate gradient (SCG) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for the period from August 1997 to December 1997 for 12 cities (Antalya, Artvin, Edirne, Kayseri, Kütahya, Van, Adana, Ankara, İstanbul, Samsun, İzmir, Diyarbaki{dotless}r) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean diffuse radiation and mean beam radiation) are used in the input layer of the network. Solar radiation is the output. However, solar radiation has been estimated as monthly mean daily sum by using Meteosat-6 satellite C3 D data in the visible range over 12 cities in Turkey. Digital counts of satellite data were converted into radiances and these are used to calculate the albedos. Using the albedo, the cloud cover index of each pixel was constructed. Diffuse and direct component of horizontal irradiation were calculated as a function of optical air mass, turbidity factor and Rayleigh optical thickness for clear-sky. Using the relation between clear-sky index and cloud cover index, the solar irradiance for any pixel is calculated for Physical method. RMS between the estimated and ground values for monthly mean daily sum with ANN and Physical method values have been found as 2.32 MJ m-2 (54 W/m2) and 2.75 MJ m-2 (64 W/m2) (training cities), 3.94 MJ m-2 (91 W/m2) and 5.37 MJ m-2 (125 W/m2) (testing cities), respectively. © 2008 Elsevier Ltd. All rights reserved

Evaluation of language and speech materials for language and speech disorders: A study of meta-synthesis

Speech is a concept that describes feelings and thoughts through verbal symbols of the organs of speech are produced. Language has a much more complex structure of the individual’s thoughts and feelings in written, verbal, gesture, or behavior with similar behavior, the symbol of creation and to comprehend. Individuals may have speech or language disorders due to physiological or psychological factors. The present study focuses on speech and language therapy and evaluation of speech therapy materials. Although the number of studies focusing on the issue is significantly higher, the studies that analyse material designs are scarce. The present study aims to accumulate the results of the studies that have evaluated materials for language and speech disorders. Related studies have been analysed and the results are reported via content analysis. © The Turkish Online Journal of Educational Technology

National assessment of sea level rise using topographic and census data for Turkish coastal zone

PubMedID: 18720019Turkish coastal zone elevation to sea level rise was illustrated by using digital elevation model and Geographical information systems methods. It was intended to determine several parameters such as population, settlements, land use, wetlands, contribution to national agricultural production and taxes at risk by using high resolution SRTM topographic, orthorectified Landsat Thematic Mapper Mosaics and census data with GIS methods within 0 - 10 m elevation of national level. All parameters were examined for coastal cities, coastal districts, settlements and villages' status. As a result of the analysis of data set, it was found that approximately 7,319 km2 of land area lies below 10 m contour line in Turkey, and is hence highly vulnerable to sea-level rise. 28 coastal cities, 191 districts and 181 villages or towns are located below 10 m contour line in study area. In the short term, for the struggle of negative impact of sea level rise, the findings suggest that the Ministry of Environment should declare new areas as protection areas and develop special environmental programs for national level. © Springer Science+Business Media B.V. 2008.National Aeronautics and Space AdministrationLandsat data was provided through NASA’s Earth Science Enterprise Scientific Data purchase Program Produced, under NASA contract, by Earth Satellite Corporation. Data set title is Geo-Cover Orthorectified Landsat Thematic Mappe

The estimation of solar radiation for different time periods

In this study, the method of Becker and Li was proposed for the estimation of monthly global land surface temperature values from meteorological satellite (NOAA-AVHRR) data. This study introduces generalized regression neural network for the estimation of solar radiation. In order to train the neural network, meteorological satellite and geographical data for the period from 2002 for short term (Adana) and 1998-2002 for long term (Izmir) in Turkey was used. Meteorological satellite and geographical data (latitude, longitude, altitude, month, and mean land surface temperature) are used in the input layer of the network. Solar radiation is the output. Root mean squared and correlation coefficient data between estimated and ground values are found with artificial neural networks values. These values have been found to be 0.0144 MJm -2 and 99.75% (short term) and 0.1381 MJm-2 and 99.26% (long term), respectively. In recent studies, there are some effective techniques about prediction solar radiation data, which is useful to the designers of solar energy systems. Nevertheless, there is no study about the prediction of solar radiation, which has used the artificial neural networks method with land surface temperature data provided from meteorological satellite data. Copyright © Taylor & Francis Group, LLC

Precipitable water modelling using artificial neural network in Çukurova region

PubMedID: 21374043Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Çukurova region, south of Turkey. We applied Levenberg-Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova region. Precipitable water was the output. Correlation coefficient (R 2) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water. © 2011 Springer Science+Business Media B.V

Forecasting of air temperature based on remote sensing

The aim of this research is to forecast air temperature based on remote sensing data. So, land surface temperature and air temperature values which were measured by Republic of Turkey Ministry of Forestry and Water Affairs (Turkish State Meteorological Service) during the period 1995-2001 at seven stations (Adana, Ankara, Bali{dotless}kesir, D{stroke}zmir, Samsun, Şanli{dotless}urfa, Van) were compared. The monthly land surface temperature and air temperature were used to have correlation coefficients over Turkey. An empirical method was obtained from equation of correlation coefficients. Separately, Price algorithm was used for the estimation of land surface temperature values to get air temperatures. Then as statistical, air temperature values, belongs to meteorological data in Turkey (26-45°E and 36-42°N) throughout 2002, were evaluated. The research results showed that accuracy of estimation of the air temperature changes from 2.453°K to 2.825°K by root mean square error

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26-45°E and 36-42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eskişehir, İstanbul, İzmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST ANN) and Becker and Li temperature(LST B-L) method values have been found as 0.077 K and 0.091 K (training stations), 0.045 K and 0.003 K (testing stations), respectively. © 2011 Indian Society of Remote Sensing

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration. © 2013 Copyright Taylor and Francis Group, LLC.Two different data sets received from the Scientific and Technological Research Council of Turkey and the Turkish State Meteorological Service were used to obtain VPD. First, raw NOAA12-14-15/AVHRR data were translated into a Level 1b format using Quorum Software, and in the second step, the brightness temperatures of channel 4 and channel 5 (range 10.3–11.3 µm and range 11.5–12.5 µm, respectively) were obtained from Level 1b data by employing the Envi 4.3 image-processing program and data received from the Scientific and Technological Research Council of Turkey during 2002